






"""simple docstring"""





from __future__ import annotations

from collections.abc import Iterable, Iterator
from dataclasses import dataclass

_SCREAMING_SNAKE_CASE   :							str          =		(3, 9, -11, 0, 7, 5, 1, -1)
_SCREAMING_SNAKE_CASE   :							Optional[int]          =		(4, 6, 2, 0, 8, 10, 3, -2)






@dataclass
class 	a		:
    SCREAMING_SNAKE_CASE		:	int
    SCREAMING_SNAKE_CASE		:	Node | None






class 	a		:






    def __init__(			self	:				Any			,		__SCREAMING_SNAKE_CASE	:				Iterable[int]   )	->						None:
      lowerCamelCase_       	=		None
      for i in sorted(__SCREAMING_SNAKE_CASE			,		reverse=__SCREAMING_SNAKE_CASE   ):
        lowerCamelCase_       	=		Node(__SCREAMING_SNAKE_CASE			,		self.head   )






    def __iter__(			self	:				Any   )	->						Iterator[int]:
      lowerCamelCase_       	=		self.head
      while node:
        yield node.data
        lowerCamelCase_       	=		node.next_node






    def __len__(			self	:				Optional[int]   )	->						int:
      return sum(1 for _ in self   )






    def __str__(			self	:				List[str]   )	->						str:
      return " -> ".join([str(__SCREAMING_SNAKE_CASE   ) for node in self]   )







def 	lowerCamelCase__			(				_lowerCamelCase			:						SortedLinkedList						,							_lowerCamelCase			:						SortedLinkedList					) ->							SortedLinkedList:
  return SortedLinkedList(list(_lowerCamelCase					) + list(_lowerCamelCase					)					)


if __name__ == "__main__":
  import doctest

  doctest.testmod()
  _SCREAMING_SNAKE_CASE   :							int          =		SortedLinkedList
  print(merge_lists(SSL(test_data_odd), SSL(test_data_even)))












"""simple docstring"""





import pandas as pd
from matplotlib import pyplot as plt
from sklearn.linear_model import LinearRegression

# Splitting the dataset into the Training set and Test set
from sklearn.model_selection import train_test_split

# Fitting Polynomial Regression to the dataset
from sklearn.preprocessing import PolynomialFeatures

# Importing the dataset
_SCREAMING_SNAKE_CASE   :							int          =		pd.read_csv(
    '''https://s3.us-west-2.amazonaws.com/public.gamelab.fun/dataset/'''
    '''position_salaries.csv'''
)
_SCREAMING_SNAKE_CASE   :							List[Any]          =		dataset.iloc[:, 1:2].values
_SCREAMING_SNAKE_CASE   :							str          =		dataset.iloc[:, 2].values


_SCREAMING_SNAKE_CASE       ,				_SCREAMING_SNAKE_CASE       ,				_SCREAMING_SNAKE_CASE       ,				_SCREAMING_SNAKE_CASE   :							Dict          =		train_test_split(X, y, test_size=0.2, random_state=0)


_SCREAMING_SNAKE_CASE   :							Optional[int]          =		PolynomialFeatures(degree=4)
_SCREAMING_SNAKE_CASE   :							Union[str, Any]          =		poly_reg.fit_transform(X)
_SCREAMING_SNAKE_CASE   :							List[str]          =		LinearRegression()
pol_reg.fit(X_poly, y)







def 	lowerCamelCase__			(				) ->							List[str]:
  plt.scatter(_lowerCamelCase						,							_lowerCamelCase						,							color='red'					)
  plt.plot(_lowerCamelCase						,							pol_reg.predict(poly_reg.fit_transform(_lowerCamelCase					)					)						,							color='blue'					)
  plt.title('Truth or Bluff (Linear Regression)'					)
  plt.xlabel('Position level'					)
  plt.ylabel('Salary'					)
  plt.show()


if __name__ == "__main__":
  viz_polymonial()

  # Predicting a new result with Polymonial Regression
  pol_reg.predict(poly_reg.fit_transform([[5.5]]))
  # output should be 132148.43750003




